Refine
Has Fulltext
- no (3)
Document Type
- Article (2)
- Doctoral Thesis (1)
Language
- English (3) (remove)
Is part of the Bibliography
- yes (3)
Keywords
- imaging (3) (remove)
Institute
- Institut für Geowissenschaften (3) (remove)
Seismology, like many scientific fields, e.g., music information retrieval and speech signal pro- cessing, is experiencing exponential growth in the amount of data acquired by modern seismo- logical networks. In this thesis, I take advantage of the opportunities offered by "big data" and by the methods developed in the areas of music information retrieval and machine learning to predict better the ground motion generated by earthquakes and to study the properties of the surface layers of the Earth. In order to better predict seismic ground motions, I propose two approaches based on unsupervised deep learning methods, an autoencoder network and Generative Adversarial Networks. The autoencoder technique explores a massive amount of ground motion data, evaluates the required parameters, and generates synthetic ground motion data in the Fourier amplitude spectra (FAS) domain. This method is tested on two synthetic datasets and one real dataset. The application on the real dataset shows that the substantial information contained within the FAS data can be encoded to a four to the five-dimensional manifold. Consequently, only a few independent parameters are required for efficient ground motion prediction. I also propose a method based on Conditional Generative Adversarial Networks (CGAN) for simulating ground motion records in the time-frequency and time domains. CGAN generates the time-frequency domains based on the parameters: magnitude, distance, and shear wave velocities to 30 m depth (VS30). After generating the amplitude of the time-frequency domains using the CGAN model, instead of classical conventional methods that assume the amplitude spectra with a random phase spectrum, the phase of the time-frequency domains is recovered by minimizing the observed and reconstructed spectrograms. In the second part of this dissertation, I propose two methods for the monitoring and characterization of near-surface materials and site effect analyses. I implement an autocorrelation function and an interferometry method to monitor the velocity changes of near-surface materials resulting from the Kumamoto earthquake sequence (Japan, 2016). The observed seismic velocity changes during the strong shaking are due to the non-linear response of the near-surface materials. The results show that the velocity changes lasted for about two months after the Kumamoto mainshock. Furthermore, I used the velocity changes to evaluate the in-situ strain-stress relationship. I also propose a method for assessing the site proxy "VS30" using non-invasive analysis. In the proposed method, a dispersion curve of surface waves is inverted to estimate the shear wave velocity of the subsurface. This method is based on the Dix-like linear operators, which relate the shear wave velocity to the phase velocity. The proposed method is fast, efficient, and stable. All of the methods presented in this work can be used for processing "big data" in seismology and for the analysis of weak and strong ground motion data, to predict ground shaking, and to analyze site responses by considering potential time dependencies and nonlinearities.
The regional patterns and timing of the Younger Dryas cooling in the North Atlantic realm were complex and are mechanistically incompletely understood. To enhance understanding of regional climate patterns, we present molecular biomarker records at subannual to annual resolution by mass spectrometry imaging (MSI) of sediments from the Lake Meerfelder Maar covering the Allerod-Younger Dryas transition. These analyses are supported by conventional extraction-based molecular-isotopic analyses, which both validate the imaging results and constrain the sources of the target compounds. The targeted fatty acid biomarkers serve as a gauge of the response of the local aquatic and terrestrial ecosystem to climate change. Based on the comparison of our data with existing data from Meerfelder Maar, we analyse the short-term environmental evolution in Western Europe during the studied time interval and confirm the previously reported delayed hydrological response to Greenland cooling. However, despite a detected delay of Western European environmental change of similar to 135 years, our biomarker data show statistically significant correlation with deuterium excess in Greenland ice core at - annual resolution during this time-transgressive cooling. This suggests a coherent atmospheric forcing across the North Atlantic realm during this transition. We propose that Western European cooling was postponed due to major reorganization of the westerlies that were intermittently forcing warmer and wetter air masses from lower latitudes to Western Europe and thus resulted in delayed cooling relative to Greenland.
Rhizosphere processes are highly dynamic in time and space and strongly depend on each other. Key factors influencing pH changes in the rhizosphere are root exudation, respiration, and nutrient supply, which are influenced by soil water content levels. In this study, we measured the real-time distribution of soil water, pH changes, and oxygen distribution in the rhizosphere of young maize plants using a recently developed imaging approach. Neutron radiography was used to capture the root system and soil water distribution, while fluorescence imaging was employed to map soil pH and soil oxygen changes. Germinated seeds of maize (Zea mays L.) were planted in glass rhizotrons equipped with pH and oxygen-sensitive sensor foils. After 20 d, the rhizotrons were wetted from the bottom and time-lapsed images via fluorescence and neutron imaging were taken during the subsequent day and night cycles for 5 d. We found higher water content and stronger acidification in the first 0.5 mm from the root surface compared to the bulk soil, which could be a consequence of root exudation. While lateral roots only slightly acidified their rhizosphere, crown roots induced stronger acidification of up to 1 pH unit. We observed changing oxygen patterns at different soil moisture conditions and increasing towards lateral as well as crown roots while extending laterally with ongoing water logging. Our work indicates that plants alter the rhizosphere pH and oxygen also depending on root type, which may indirectly arise also from differences in age and water content changes. The results presented here were possible only by combining different imaging techniques to examine profiles at the root-soil interface in a comprehensive way during wetting and drying.